A large variety of fairness metrics have been introduced to quantify algorithmic biases, as presented in [1]}, [2]}, [3]}, [4]} and references therein. They quantify different levels of relationships between a given sensitive variable \(A\)  and outputs of the algorithm. Yet, as fairness is a polysemous word, there exist multiple metrics, each one focusing on a particular definition of bias and, unfortunately, all of them are not necessarily compatible with each other, as recently discussed in [5]} or [6]}. Therefore, it is essential for someone evaluating the bias of a model to understand what the fairness metrics really capture. They conform to different definitions of biases given in the previous section and can be decomposed as follows.
